CN111579637B - Nondestructive testing method and device for detecting and distinguishing internal and external defects of steel wire rope - Google Patents
Nondestructive testing method and device for detecting and distinguishing internal and external defects of steel wire rope Download PDFInfo
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Abstract
The invention provides a nondestructive testing method and a nondestructive testing device for detecting and distinguishing internal and external defects of a steel wire rope, wherein the method comprises the following steps: collecting a magnetic flux signal and a magnetic leakage signal of a steel wire rope to be detected; preprocessing a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be detected; comparing the preprocessed magnetic flux signal and the preprocessed magnetic flux leakage signal with a preset magnetic flux signal threshold value and a preset magnetic leakage signal threshold value respectively, and calculating the defect position; extracting a defect magnetic flux signal and a defect magnetic flux leakage signal according to the defect position; calculating the defect width flw of the steel wire rope to be tested according to the defect magnetic flux signal and the defect magnetic leakage signal; calculating the defect section loss fs of the detected steel wire rope according to the defect width flw of the detected steel wire rope; internal and external defects are calculated and distinguished. By adopting the technical scheme of the invention, not only can all types of defects of the steel wire rope be identified, but also internal and external defects can be distinguished, and the defects can be accurately and quantitatively detected, so that the burial depth of the defects can be accurately calculated.
Description
Technical Field
The invention belongs to the technical field of nondestructive testing, and particularly relates to a nondestructive testing method and a nondestructive testing device for detecting and distinguishing internal and external defects of a steel wire rope.
Background
The steel wire rope as a flexible part has strong load-carrying capacity, outstanding flexibility and excellent working stability, and is widely applied to the fields of mines, shipping, buildings, transportation and the like. However, the steel wire rope inevitably generates fatigue damage such as abrasion, wire breakage, corrosion and the like in the long-term use process, the damage degree tends to be serious along with the prolonging of the service cycle, if the steel wire rope cannot be replaced before the whole rope is broken, the safety production is seriously influenced, even the equipment and personal safety are threatened, and huge economic loss and adverse social influence are caused. Defects of the steel cord can be classified into external defects and internal defects. The detection of defects becomes increasingly difficult as the depth of defect burial increases. The existing detection method cannot quantitatively detect the internal defects and distinguish the internal defects from the external defects.
The electromagnetic detection method is the most effective method at the present stage, and can be divided into saturated excitation and unsaturated excitation according to excitation conditions. The unsaturated excitation detection has strict requirements on a sensor, environment, a mode and the like, can not accurately carry out quantitative detection, and can not be applied to actual detection. The saturated excitation detection can avoid the defects, improve the precision of quantitative detection and be better applied to actual detection.
The saturated excitation detection mainly comprises two methods of magnetic flux detection and magnetic flux leakage detection. The magnetic flux detection mainly detects the amount of change in magnetic flux of an object to be detected, the magnetic flux including main magnetic flux, leakage magnetic flux, yoke magnetic flux, and the like. The method has the advantages that: the detected flux value is related to the cross-section loss area of the measured object; whether the defect is outside or inside, the magnetic flux nondestructive detection can be detected; however, when the axial width of the defect is small, its detection capability is very low, and it is impossible to quantitatively detect and distinguish the internal defect. The existing magnetic flux detection method cannot quantitatively detect all defects and cannot calculate the defect burial depth. The magnetic leakage detection mainly detects the magnetic leakage field intensity on the surface of a detected object through a sensor array. The magnetic flux leakage detection has high recognition rate on the defect with small axial width, and the detection on the defect width is also more accurate. But for the defect with larger axial width, the information of the defect can not be accurately identified; the defect burying depth seriously affects the detection precision, and all defects cannot be detected quantitatively.
Therefore, the existing detection method cannot distinguish the internal and external defects of the steel wire rope, has low detection precision and cannot calculate the defect burial depth.
Disclosure of Invention
Aiming at the technical problems, the invention discloses a nondestructive testing method and a nondestructive testing device for detecting and distinguishing the internal and external defects of a steel wire rope, which can distinguish the internal and external defects of the steel wire rope and calculate the defect burying depth so as to enable the detection to be more accurate.
In contrast, the technical scheme adopted by the invention is as follows:
a nondestructive testing method for detecting and distinguishing inner and outer defects of a steel wire rope comprises the following steps:
step S10, collecting a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be tested;
step S20, preprocessing the magnetic flux signal and the magnetic leakage signal of the tested steel wire rope;
step S30, comparing the preprocessed magnetic flux signal and magnetic leakage signal with a preset magnetic flux signal threshold and a preset magnetic leakage signal threshold respectively, and calculating the defect position;
step S40, extracting a defect magnetic flux signal and a defect magnetic flux leakage signal according to the defect position;
step S50, calculating the defect width flw of the detected steel wire rope according to the defect magnetic flux signal and the defect magnetic flux leakage signal;
step S60, calculating the defect section loss fs of the detected steel wire rope according to the defect width flw of the detected steel wire rope;
step S70, distinguishing internal and external defects:
calculating a waveform peak value FV of the defect magnetic leakage signal by using a formula FV-FL, wherein FH is the waveform peak value of the defect magnetic leakage signal, and FL is the waveform valley value of the defect magnetic leakage signal;
designing a relation function ffs as f2(FV, flw), wherein f2 is a training multiple equation group or a multilayer neural network, and ffs is a virtual section loss amount;
substituting the defect width flw obtained in the step S50 and the waveform peak value FV of the defect magnetic leakage signal into the design relation function to calculate ffs;
comparing ffs and fs, if | fs-ffs | is more than mu, the defect is an internal defect, otherwise, the defect is an external defect;
where μ is a preset defect determination value.
Further, μmay be set as a section loss rate of one broken wire, or may be set according to actual conditions.
Furthermore, the steel wire rope to be detected is excited to a saturated or approximately saturated state, and then a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be detected are collected.
As a further improvement of the present invention, the nondestructive testing method for detecting and distinguishing the internal and external defects of the steel wire rope further comprises:
step S80, calculating defect burying depth:
according to the result of S70, if the defect is an external defect, the buried depth of the defect is 0, otherwise the buried depth of the defect is not 0, performing the following steps;
the buried depth fd of the defect is calculated according to the following formula,
fd＝f3(fs,ffs,flw)，
wherein f3 is a multiple equation group or a multilayer neural network of training, flw is the defect width of the steel wire rope to be detected, fs is the defect section loss of the steel wire rope to be detected, and ffs is the virtual section loss.
As a further improvement of the present invention, in step S70, the trained multiple equation set or the multi-layer neural network f2 is obtained by the following steps:
step 721: designing x defect widths, y defect section loss amounts and total x multiplied by y standard surface defects, wherein x and y are natural numbers;
step 722: calculating the x y standard surface defects through the steps S10 to S60 to obtain corresponding peak values of the leakage flux waveform of the defects;
step 723: and training to obtain a multiple equation set or a multilayer neural network f2 by taking the obtained defect leakage magnetic waveform peak value and the defect width of the standard surface defect as input independent variables and ffs as output standard quantities.
As a further improvement of the present invention, in step S80, the trained multiple equation set or the multi-layer neural network f3 is obtained by the following steps:
step 821: designing x defect widths, y defect section loss amounts, z different buried depths, and total x multiplied by y multiplied by z standard defects, wherein x, y and z are natural numbers;
step 822: calculating the standard defects of x, y and z to obtain corresponding ffs through the steps S10 to S70;
step 823: and training to obtain a multiple equation set or a multilayer neural network f3 by taking the obtained ffs, the defect section loss amount and the defect width as input independent variables and the defect burial depth as an output standard quantity.
As a further improvement of the present invention, step S10 includes acquiring a magnetic flux signal of the steel wire rope to be tested by a magnetic flux detection sensor, acquiring a magnetic flux leakage signal of the steel wire rope to be tested by a magnetic field strength detection sensor, and performing preliminary processing on the acquired magnetic flux signal of the steel wire rope to be tested to eliminate the influence of the steel wire rope speed;
the magnetic flux signal is processed by integrating the integrator with time according to the following formula, then the data is collected by sampling in equal space,
or the following formula is adopted to carry out equidistant integration processing and acquisition on the magnetic flux signals through the space of the integrator;
y ═ Sdl, where Y is the magnetic flux signal of the detected steel wire rope after the preliminary processing, dl represents the differential to the spatial distance, and S is the collected magnetic flux signal of the detected steel wire rope.
As a further improvement of the present invention, the step S20 of preprocessing the magnetic flux signal of the detected steel wire rope includes performing outlier rejection, noise filtering, baseline elimination, etc. on the magnetic flux signal of the detected steel wire rope, which can improve the signal-to-noise ratio of the magnetic flux signal and is more beneficial to feature extraction of the signal.
As a further improvement of the present invention, the step of performing outlier rejection on the magnetic flux signal of the detected steel wire rope comprises:
removing wild points of the magnetic flux signal Y of the steel wire rope to be detected, setting Y (i) as an ith magnetic flux acquisition signal, and when Y (i) is far greater than the values of the front and rear magnetic flux signals, Y (i-1) + Y (i +1)](i is 1,2, …, N), and obtaining a signal Y after the outlier rejection processing1(i) And N is the total number of sampling points.
As a further improvement of the present invention, the step of performing noise filtering on the magnetic flux signal of the detected steel wire rope includes:
and performing noise filtering on the magnetic flux signal of the steel wire rope to be detected by adopting self-adaptive filtering, wavelet transformation, smoothing filtering or empirical mode decomposition, wherein a calculation formula for performing noise filtering on the magnetic flux signal of the steel wire rope to be detected by adopting smoothing filtering is as follows:
wherein N is the number of data for averaging, and N is the number of total sampling points.
As a further improvement of the present invention, the step of performing baseline cancellation on the magnetic flux signal of the tested steel wire rope comprises:
performing baseline elimination on the magnetic flux signal of the steel wire rope to be detected by adopting envelope spectrum extraction, wavelet decomposition, window averaging or empirical mode decomposition, wherein the step of performing baseline elimination on the magnetic flux signal of the steel wire rope to be detected by adopting empirical mode decomposition comprises the following steps of:
finding the signal data sequence Y2(i) Fitting all the maximum value points and minimum value points to an upper envelope line and a lower envelope line of the original sequence by a cubic spline function respectively; the mean of the upper and lower envelopes is m 1; data sequence Y2(i) Subtracting m1 to obtain a new sequence Y with low frequency subtracted3(i) I.e. Y3(i)＝Y2(i)－m1。
As a further improvement of the present invention, in step S20, the preprocessing of the magnetic leakage signal of the measured steel wire rope includes performing outlier rejection, noise filtering, baseline elimination, and spike noise filtering on each magnetic leakage signal of the measured steel wire rope, so as to improve the signal-to-noise ratio of the magnetic leakage signal and facilitate the feature extraction of the signal.
As a further improvement of the present invention, the step of eliminating the magnetic leakage signal of each path of the tested steel wire rope comprises:
eliminating the wild points of each path of magnetic leakage signal X, and setting Xi,jIs the jth sampling value of the ith Hall sensor when X isi,jWhen the magnetic flux leakage signal value is far larger than the front and back magnetic flux leakage signal values:
obtaining a signal X after the outlier rejectioni,j。
As a further improvement of the present invention, the step of performing noise filtering on each path of leakage magnetic signal of the tested steel wire rope includes:
carrying out noise filtering on each path of magnetic flux leakage signal of the steel wire rope to be tested by adopting self-adaptive filtering, or wavelet transformation, or smooth filtering, or empirical mode decomposition; the calculation formula for carrying out noise filtering on each path of magnetic leakage signal of the detected steel wire rope by adopting smooth filtering is as follows:
wherein, N is the data number of averaging, N is the total number of sampling points, and k is the magnetic field intensity detection sensor path number of magnetic leakage signal of the detected steel wire rope.
As a further improvement of the present invention, the step of performing baseline cancellation on each magnetic leakage signal of the tested steel wire rope includes:
performing baseline elimination on each path of magnetic leakage signal of the tested steel wire rope by adopting envelope spectrum extraction, wavelet decomposition, window averaging or empirical mode decomposition; the step of eliminating the baseline of each path of magnetic leakage signal of the tested steel wire rope by adopting empirical mode decomposition comprises the following steps:
finding out all maximum value points and minimum value points of the magnetic leakage signal data sequence Xy after the outlier rejection processing, respectively fitting the maximum value points and the minimum value points to an upper envelope line and a lower envelope line of an original sequence by using a cubic spline function, wherein the mean value of the upper envelope line and the lower envelope line is m 1; subtracting n1 from the original data sequence yields a new sequence X1 with low frequency subtracted, i.e. X1-Xy-n 1.
As a further improvement of the present invention, the step of filtering the leakage magnetic signal of each path of the tested steel wire rope by using the strand noise includes:
adopt wavelet decomposition, or empirical mode decomposition, or adaptive filtering, or the gradient method is right every way magnetic leakage signal of being surveyed wire rope carries out the spike noise filtering, wherein, adopt the gradient method right every way magnetic leakage signal of being surveyed wire rope carries out the step of spike noise filtering and includes:
using gradient method to realize first order differentiation of image, for image X1(x, y) whose gradient at coordinate (x, y) is a two-dimensional column vector representation:
the modulus of this vector is:
summing the multi-path magnetic leakage signals to obtain magnetic leakage sum signal X2。
As a further improvement of the present invention, the calculating of the defect position in step S30 includes the steps of:
step S31, setting a defect magnetic flux signal preset threshold value mp of the detected steel wire rope, wherein mp is the peak value of the magnetic flux signal with the minimum defect;
step S32, comparing the magnetic flux signal of the detected steel wire rope with a defect magnetic flux signal preset threshold value mp, and recording a plurality of groups of magnetic flux sampling points of which the continuous magnetic flux signals are greater than the defect magnetic flux signal preset threshold value mp, wherein the axial coordinates of the plurality of groups of magnetic flux sampling points are [ c11, c12 … … c1a ], [ c21, c22 … … c2b ] and … …;
step S33, calculating an average value of the axial coordinates of each set of magnetic flux collection points, where c1 is (c11+ c12+ … … + c1a)/a, c2 is (c21+ c22+ … … + c2b)/b, … …, to obtain a sequence (c1, c2, … …);
step S34, setting a defect magnetic leakage signal preset peak-to-peak value threshold vp of the detected steel wire rope, wherein vp is a magnetic leakage signal preset peak-to-peak value of the minimum defect;
comparing the magnetic leakage signal of the tested steel wire rope with a preset peak-to-peak threshold vp of the defect magnetic leakage signal, recording a plurality of groups of continuous magnetic leakage sampling points of which the magnetic leakage signals are greater than the preset peak-to-peak threshold vp of the defect magnetic leakage signal, wherein the axial coordinates of the plurality of groups of magnetic leakage sampling points are [ d11, d12 … … d1e ], [ d21, d22 … … d2f ] and … …, and obtaining sequences (d1, d2 and … …);
step S35, calculating an average value of the maximum and minimum values of the axial coordinates of each group of leakage flux collection points, namely d1 ═ d11+ d1e)/2, d2 ═ d21+ d2f)/2, … …;
and step S36, comparing the sequences (c1, c2, … …) with the sequences (d1, d2, … …), if the | ci-dj | is less than M, wherein M is the distance between steel wire strands, keeping ci and abandoning dj, otherwise, keeping both ci and dj, and obtaining the calculation result as the defect position.
As a further improvement of the present invention, step S40, the extracting the defect flux signal includes: according to the position information of each group of magnetic flux sampling points, FM points are extracted forwards and backwards from the magnetic flux signals of the steel wire rope to serve as defect magnetic flux signals, wherein FM is NO multiplied by SM, SM is the number of sampling points with 1 strand pitch, and NO is a natural number of 5-10;
extracting the defect leakage magnetic signal includes: according to the position information of each group of magnetic leakage acquisition points, LFM points are extracted forwards and backwards from the magnetic leakage signal of the steel wire rope to serve as a defect magnetic leakage signal, the LSM is the number of sampling points with 1 strand pitch, the LFM is LNO multiplied by LSM, and the LNO is a natural number of 5-10.
As a further improvement of the present invention, the step of calculating the defect width flw of the measured steel wire rope in the step S50 includes the following steps:
step S51, first, according to the formula h (S) ═ df (S)/ds (S) ═ 1,2, …, k), the differentiation result h (S) of the defect magnetic flux signal of the steel wire rope to be tested is solved, where k is the data number of the defect magnetic flux signal, and f (S) is the data of the defect magnetic flux signal; then, according to the position of the waveform peak point of the defect magnetic flux signal, the position of the maximum value of h(s) is taken forward, the position of the minimum value of h(s) is taken backward, and the distance between the maximum value and the minimum value is calculated as the waveform width value Ylw of the defect magnetic flux signal;
step S52, calculating the distance between the maximum value and the minimum value of the defect leakage magnetic signal according to the position of the waveform peak point of the defect leakage magnetic signal as the waveform width value Xlw of the defect magnetic flux signal;
s53, when the | Ylw-Xlw | is smaller than M, and M is the distance between steel wire strands, selecting the larger value of YLw and Xlw as the defect width flw; when | Ylw-Xlw | ≧ M, the defect width flw ═ (Ylw + Xlw)/2-LF, where LF is the distance of the sensor from the surface of the steel cord.
As a further improvement of the present invention, step S60, the step of calculating the defect section loss fs of the measured steel wire rope includes the following steps:
step S61, calculating a waveform peak value VPP of the obtained waveform peak value T and waveform baseline value L of the defect magnetic flux signal by a formula VPP ═ T-L |;
step S62, designing a relationship function fs ═ f1(VPP, flw), where f1 is a training multiple power equation group or a multilayer neural network;
and step S63, substituting the defect width value flw obtained in step S50 and the waveform peak value VPP of the defect magnetic flux signal of the step S61 into the multiple power module or the multilayer neural network in step S62, and calculating the defect section loss fs of the steel wire rope.
As a further improvement of the present invention, the trained multiple equation set or the multi-layer neural network f1 in step S62 is obtained by the following steps:
step S621, designing x defect widths and y defect section loss amounts, wherein x is multiplied by y standard surface defects in total, and x and y are natural numbers;
step S622: calculating x y standard surface defects through steps S10 to S60 to obtain corresponding defect magnetic flux waveform peak-to-peak values;
step S623: and training to obtain a multiple equation set or a multilayer neural network f1 by taking the corresponding defect magnetic flux waveform peak value and the defect width of the standard surface defect as input independent variables and taking the defect section loss as output standard quantities.
The invention also discloses a nondestructive testing device for detecting and distinguishing the internal and external defects of the steel wire rope, which comprises:
the excitation structure is used for exciting the steel wire rope to a saturated or approximately saturated state;
the magnetic flux detection sensor is used for acquiring a magnetic flux signal of the steel wire rope to be detected;
the magnetic field intensity detection sensor is used for acquiring a magnetic leakage signal of the detected steel wire rope;
and a signal acquisition and processing system which processes by the nondestructive detection method for detecting and distinguishing the internal and external defects of the steel wire rope;
the signal acquisition and processing system comprises a signal acquisition unit, a signal preprocessing unit, a defect position calculating unit, a defect signal extracting unit, a defect width calculating unit, a defect section loss amount calculating unit, an internal and external defect distinguishing unit and a defect burying depth calculating unit, wherein,
the signal acquisition unit is used for acquiring a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be detected,
the signal preprocessing unit is used for preprocessing a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be detected;
the defect position calculation unit is used for comparing the preprocessed magnetic flux signal and the preprocessed magnetic flux leakage signal with a preset magnetic flux signal threshold and a preset magnetic leakage signal threshold respectively to calculate the defect position;
the defect signal extraction unit is used for extracting a defect magnetic flux signal and a defect magnetic flux leakage signal according to the defect position;
the defect width calculation unit is used for calculating the defect width flw of the steel wire rope to be detected according to the defect magnetic flux signal and the defect magnetic leakage signal;
the defect section loss amount calculation unit is used for calculating the defect section loss amount fs of the detected steel wire rope according to the defect width flw of the detected steel wire rope;
the internal and external defect distinguishing unit is used for distinguishing whether the steel wire rope is an internal defect or an external defect according to the defect width flw of the steel wire rope to be detected and the waveform peak value FV of the defect magnetic flux signal;
the defect burying depth calculating unit is used for calculating the defect burying depth according to the defect width flw of the detected steel wire rope, the defect section loss fs and the virtual section loss ffs of the detected steel wire rope.
Compared with the prior art, the invention has the beneficial effects that:
by adopting the technical scheme of the invention, the steel wire rope is excited to a saturated or approximately saturated state, and by detecting and collecting the magnetic flux and the magnetic leakage field quantity of the steel wire rope, all types of defects of the steel wire rope can be identified and internal and external defects can be distinguished through calculation and analysis; the method can not only accurately and quantitatively detect the defects, but also accurately calculate the buried depth of the defects, and has higher quantitative precision.
Drawings
FIG. 1 is a schematic view of a nondestructive testing apparatus for detecting and distinguishing internal and external defects of a steel wire rope according to the present invention.
FIG. 2 is a schematic view of a sensor of a nondestructive testing apparatus for detecting and distinguishing defects inside and outside a wire rope according to the present invention.
FIG. 3 is a flow chart of a non-destructive inspection method for detecting and distinguishing internal and external defects of a wire rope according to the present invention.
Fig. 4 is a schematic diagram of a defect flux signal obtained by an embodiment of the present invention.
Fig. 5 is a schematic diagram of a defect leakage magnetic signal obtained by the embodiment of the present invention.
The reference numerals include: 1-excitation structure, 2-sensor, 21-magnetic flux detection sensor, 22-magnetic field intensity detection sensor, and 3-signal acquisition and processing system.
Detailed Description
Preferred embodiments of the present invention are described in further detail below.
As shown in fig. 1, a nondestructive testing apparatus for detecting and distinguishing inner and outer defects of a steel wire rope includes:
the excitation structure 1 is used for exciting the steel wire rope to a saturated or approximately saturated state;
a sensor 2, as shown in fig. 2, including a magnetic flux detection sensor 21 for acquiring a magnetic flux signal of a steel wire rope to be measured, and a magnetic field strength detection sensor 22 for acquiring a magnetic flux leakage signal of the steel wire rope to be measured;
and a signal acquisition and processing system 3, which comprises a signal acquisition unit, a signal preprocessing unit, a defect position calculation unit, a defect signal extraction unit, a defect width calculation unit, a defect section loss amount calculation unit, an internal and external defect distinguishing unit and a defect burial depth calculation unit.
Wherein the signal acquisition unit is used for acquiring a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be detected,
the signal preprocessing unit is used for preprocessing a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be detected;
the defect position calculation unit is used for comparing the preprocessed magnetic flux signal and the preprocessed magnetic flux leakage signal with a preset magnetic flux signal threshold and a preset magnetic leakage signal threshold respectively to calculate the defect position;
the defect signal extraction unit is used for extracting a defect magnetic flux signal and a defect magnetic flux leakage signal according to the defect position;
the defect width calculation unit is used for calculating the defect width flw of the steel wire rope to be detected according to the defect magnetic flux signal and the defect magnetic leakage signal;
the defect section loss amount calculation unit is used for calculating the defect section loss amount fs of the detected steel wire rope according to the defect width flw of the detected steel wire rope;
the internal and external defect distinguishing unit is used for distinguishing whether the steel wire rope is an internal defect or an external defect according to the defect width flw of the steel wire rope to be detected and the waveform peak value FV of the defect magnetic flux signal;
and the defect burial depth calculating unit is used for calculating the defect burial depth according to the defect width flw of the detected steel wire rope and the defect section loss fs and ffs of the detected steel wire rope.
The signal acquisition and processing system is processed by a nondestructive testing method for detecting and distinguishing the internal and external defects of the steel wire rope as follows.
Specifically, as shown in fig. 3, the nondestructive testing method for detecting and distinguishing the inner and outer defects of the steel wire rope comprises the following steps:
step 10: collecting a detected steel wire rope detection signal;
step 20: preprocessing a detected signal of the detected steel wire rope;
step 30: calculating the position of the defect;
step 40: extracting a defect signal;
step 50: calculating the width of the defect;
step 60: calculating the defect section loss rate;
step 70: distinguishing internal and external defects;
step 80: and calculating the defect burying depth.
Wherein, gather in step10 and surveyed wire rope detected signal and include magnetic flux signal and magnetic leakage signal, specifically include:
acquiring a magnetic flux signal of a steel wire rope to be detected through a magnetic flux detection sensor, and acquiring a magnetic leakage signal of the steel wire rope to be detected through a magnetic field intensity detection sensor; the magnetic flux signal is influenced by the speed of the steel wire rope, and the speed of the steel wire rope cannot be accurately detected in real time, so that the influence of the speed of the steel wire rope needs to be eliminated. Integrating the magnetic flux signal S with time through an integrator, and then sampling and acquiring data through equal space or performing equidistant integration processing and acquisition on the magnetic flux signal S through the integrator, wherein the formula is as follows:
or Y ═ Sdl
Where dt represents the derivative over time, N is the total number of sample points, and dl represents the derivative over spatial distance.
In step20, preprocessing the detected steel wire rope detection signal comprises preprocessing a magnetic flux signal and preprocessing a magnetic leakage signal, wherein the preprocessing of the magnetic flux signal comprises outlier rejection, noise filtering, baseline elimination and the like, so that the signal-to-noise ratio of the magnetic flux signal can be improved, and the characteristic extraction of the signal is facilitated. The following are the specific steps:
step 21: removing wild points from the magnetic flux signal Y, setting Y (i) as the ith magnetic flux collecting signal, and when Y (i) is far greater than the values of the front and rear magnetic flux signals, Y (i) ([ Y (i-1) + Y (i + 1))](i is 1,2, …, N), obtained after outlier rejection processingSignal Y1(i) N is the total number of sampling points;
step 22: and performing noise filtering on the magnetic flux signal of the steel wire rope to be detected by adopting self-adaptive filtering, wavelet transformation, smoothing filtering or empirical mode decomposition, wherein a calculation formula for performing noise filtering on the magnetic flux signal of the steel wire rope to be detected by adopting smoothing filtering is as follows:
wherein N is the number of data for averaging, and N is the total number of sampling points;
step 23: performing baseline elimination on the above signals, wherein the baseline elimination is performed by using methods including but not limited to envelope spectrum extraction, wavelet decomposition, window averaging, empirical mode decomposition, and the like, and the following method is a method of empirical mode decomposition: find out the above signal data sequence Y1(i) Fitting all the maximum value points and minimum value points to the upper envelope line and the lower envelope line of the original sequence by a cubic spline function; the mean of the upper and lower envelope lines is m 1; subtracting m1 from the data sequence yields a new sequence Y minus the low frequency2(i) I.e. Y2(i)＝Y1(i) -m 1. In the formula, N is the number of data for averaging, and N is the number of total sampling points;
the preprocessing of the magnetic leakage signal mainly comprises the steps of carrying out outlier rejection, noise filtering, baseline elimination, spike filtering and the like on each path of magnetic leakage signal, so that the signal-to-noise ratio of the magnetic leakage signal can be improved, and the characteristic extraction of the signal is facilitated. The following are the specific steps:
step 24: eliminating the wild points of each path of magnetic leakage signal X, and setting Xi,jIs the jth sampling value of the ith Hall sensor when X isi,jWhen the magnetic flux leakage signal value is far larger than the front and back magnetic flux leakage signal values:
obtaining a signal X after the outlier rejectioni,j；
Step 25: each path of signals is subjected to noise filtering, the noise filtering adopts a method including but not limited to adaptive filtering, wavelet transformation, smoothing filtering, empirical mode decomposition and the like, and the following is a processing method of smoothing filtering:
in the formula, N is the number of data for averaging, N is the total sampling point number, and k is the number of sensor paths;
step 26: performing baseline elimination on each path of magnetic leakage signal of the steel wire rope to be detected, and performing baseline elimination on each path of magnetic leakage signal of the steel wire rope to be detected by adopting envelope spectrum extraction, wavelet decomposition, window averaging or empirical mode decomposition; the step of eliminating the baseline of each path of magnetic leakage signal of the tested steel wire rope by adopting empirical mode decomposition comprises the following steps: finding out all maximum value points and minimum value points of the magnetic leakage signal data sequence Xy after the outlier rejection processing, respectively fitting the maximum value points and the minimum value points to an upper envelope line and a lower envelope line of an original sequence by using a cubic spline function, wherein the mean value of the upper envelope line and the lower envelope line is n 1; subtracting n1 from the original data sequence to obtain a new sequence X with low frequency subtracted1I.e., X1 ═ Xy-n 1.
Step 27: it is right every way magnetic leakage signal of being surveyed wire rope carries out the filtering of strand wave noise, adopts wavelet decomposition, or empirical mode decomposition, or adaptive filtering, or the gradient method is right every way magnetic leakage signal of being surveyed wire rope carries out the filtering of strand wave noise, and wherein, it is right to adopt the gradient method every way magnetic leakage signal of being surveyed wire rope carries out the step of strand wave noise filtering and includes:
using gradient method to realize first order differentiation of image, for image X1(x, y) whose gradient at coordinate (x, y) is a two-dimensional column vector representation:
the modulus of this vector is:
summing the multi-path magnetic leakage signals to obtain magnetic leakage sum signal X2。
The method of calculating the defect location described in step30 is as follows:
step 31: setting a defect magnetic flux signal preset threshold value mp of a detected steel wire rope, wherein the mp is a magnetic flux signal peak value of the minimum defect;
step 32: comparing the magnetic flux signal of the tested steel wire rope with a preset threshold value mp, and recording a plurality of groups of continuous magnetic flux sampling points, wherein the axial coordinates of the plurality of groups of collecting points are [ c11, c12 … … c1a ], [ c21, c22 … … c2b ], … …;
step 33: calculating the average value of the axial coordinates of each group of magnetic flux collection points, wherein c1 is (c11+ c12+ … … + c1a)/a, c2 is (c21+ c22+ … … + c2b)/b, … …;
step 34: setting a defect magnetic leakage signal preset peak-to-peak threshold value vp of the detected steel wire rope, wherein vp is a magnetic leakage signal preset peak-to-peak value of the minimum defect;
step 34: comparing the magnetic flux signal of the tested steel wire rope with a preset threshold vp, and recording multiple groups of continuous magnetic flux leakage sampling points, wherein the axial coordinates of the multiple groups of collecting points are [ d11, d12 … … d1e ], [ d21, d22 … … d2f ], … …;
step 35: calculating the average value of the maximum and minimum values of the axial coordinates of each group of magnetic leakage collection points, namely d 1-d 11+ d1e)/2, d 2-d 21+ d2f)/2, … …;
step 36: comparing (c1, c2, … …) with (d1, d2, … …), if a value which is closer is | ci-dj | < M, M is the distance between steel wire strands, ci is reserved, dj is discarded, otherwise, both ci and dj are reserved, and the calculation result is the defect position.
step40 is that extracting the defect signal includes extracting the magnetic flux signal of the defect and extracting the leakage magnetic flux signal of the defect, and specifically includes:
step 41: according to the position information of each group of magnetic flux acquisition points, FM points are extracted forward and backward from the magnetic flux signals of the steel wire rope, wherein SM is the number of sampling points with 1 strand pitch, FM is NO multiplied by SM, NO is 5-10, and the FM points can also be set according to the actual detection condition, and the intercepted data are used as defect magnetic flux signals;
step 42: according to the position information of each group of magnetic leakage acquisition points, LFM points are extracted forwards and backwards from magnetic leakage signals of the steel wire rope, LSM is the number of sampling points with 1 strand pitch, LFM is LNO multiplied by LSM, LNO is 5-10, the LFM can also be set according to actual detection conditions, and intercepted data are used as defect magnetic leakage signals.
In step50, the method for calculating the defect width is as follows:
step 51: firstly, solving a differentiation result h(s) of the defect magnetic flux signal by a formula h(s) ═ df (s)/ds(s) ═ 1,2, …, k), wherein k is the data number of the defect magnetic flux signal, and f(s) is the data of the defect magnetic flux signal; then, according to the position of the peak point of the waveform of the defect magnetic flux signal, the position of the maximum value of h(s) is taken forward, the position of the minimum value of h(s) is taken backward, and the distance between the maximum value and the minimum value is calculated as the waveform width value Ylw of the defect magnetic flux signal, as shown in fig. 4;
step 52: calculating the distance between the maximum value and the minimum value of the defect magnetic leakage signal according to the position of the peak point of the waveform of the defect magnetic leakage signal to be used as the waveform width value Xlw of the defect magnetic flux signal;
step 53: when the absolute value of Ylw-Xlw is less than M, M is the distance between steel wire strands, and the larger value is selected as the defect width flw; when | Ylw-Xlw | ≧ M, the defect width flw is (Ylw + Xlw)/2-LF, and LF is the distance from the sensor to the surface of the steel wire rope.
In step60, the method for calculating the defect section loss amount is as follows:
step 61: obtaining a waveform peak value T and a waveform baseline value L of the defect magnetic flux signal, and calculating a waveform peak value VPP of the defect magnetic flux signal through a formula VPP (VPP-T-L);
step 62: designing a relation function fs ═ f1(VPP, flw), wherein f1 is a training multiple power equation group or a multilayer neural network;
step 63: and substituting the waveform peak-to-peak values VPP of the defect magnetic flux signals of the defect width value flw and the defect magnetic flux signal of the S61 obtained in the step S50 into the multiple power equation group or the multilayer neural network obtained in the step S62 to calculate the accurate defect metal section loss amount fs.
The training multiple equation set or multi-layer neural network f1 described in step62 is obtained by:
step 621: designing x widths flw, y metal section loss areas fs, and total x y standard surface defects, wherein x and y are natural numbers;
step 622: calculating x y standard flaws in steps S10-S60 to obtain corresponding defect magnetic flux waveform peak value VPP;
step 623: and training to obtain a multiple equation set or a multilayer neural network f1 by taking the waveform peak value VPP and the standard flaw waveform width value flw as input independent variables and the cross-sectional loss area fs as an output standard quantity.
step70, the method for distinguishing the internal and external defects comprises the following steps:
step 71: obtaining a waveform peak value FH and a waveform valley value FL of the magnetic leakage signal, and calculating a waveform peak value FV of the magnetic leakage signal according to a formula FV-FL, as shown in fig. 5;
step 72: designing a relation function ffs-f 2(FV, flw), wherein f2 is a training multiple equation set or a multi-layer neural network;
step 73: substituting the waveform peak value FV of the defect leakage magnetic signal of the defect width value flw and the defect leakage magnetic signal of S71 obtained in the step S50 into the multiple equation group or the multilayer neural network obtained in the step S72, and calculating to obtain a virtual section loss amount ffs;
step 74: comparing fs in ffs and S63, if | fs-ffs | is greater than μ, μ can be set as the section loss rate of a broken filament, or set according to actual conditions, the defect is an internal defect, otherwise, the defect is an external defect.
Wherein the multiple equation system or the multilayer neural network f2 trained by step72 is obtained by the following method:
step 721: designing x widths flw, y metal section loss areas fs, and total x y standard surface defects, wherein x and y are natural numbers;
step 722: calculating x y standard injuries through the steps S10 to S60 to obtain corresponding defect magnetic leakage waveform peak values FV;
step 723: and training to obtain a multiple equation set or a multilayer neural network f2 by taking the waveform peak value FV and the standard flaw waveform width value flw as input independent variables and ffs as output standard quantities.
In step80, the method for calculating the defect burying depth is as follows:
step 81: according to the result of S70, if the defect is an outer portion, the buried depth of the defect is 0, otherwise, the buried depth of the defect is not 0, and the step S82 is performed;
step 82: designing a relation function fd ═ f3(fs, ffs, flw), wherein f3 is a training multiple power equation group or a multilayer neural network;
step 83: and substituting the defect width value flw obtained in S50, fs obtained in S60 and ffs obtained in S70 into the multiple equation group or the multilayer neural network in the step S82, and calculating the buried depth fd of the defect.
Wherein the step82 training multiple equation system or the multilayer neural network f3 method is as follows:
step 821: designing x widths flw, y metal section loss areas fs, z different buried depths fd, and total x multiplied by y multiplied by z standard defects, wherein x, y and z are natural numbers;
step 822: calculating the standard wounds with the x y x z number through the steps from S10 to S70 to obtain corresponding ffs;
step 823: and (3) taking ffs, fs and flw as input independent variables and fd as an output standard quantity, and training to obtain a multiple-order equation set or a multilayer neural network f 3.
By adopting the technical scheme of the embodiment, the steel wire rope is excited to saturation or approximate saturation, the magnetic flux and magnetic leakage signals of the steel wire rope are collected and preprocessed, the defect magnetic signal is extracted, the magnetic flux signal and the magnetic leakage signal of the defect are respectively calculated and analyzed, the calculation results of the two signals are fused and analyzed, the internal defect and the external defect can be distinguished, the defect is further quantitatively calculated, and the finally obtained defect burial depth result is accurate.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A nondestructive detection method for detecting and distinguishing inner and outer defects of a steel wire rope is characterized by comprising the following steps:
step S10, collecting a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be tested;
step S20, preprocessing the magnetic flux signal and the magnetic leakage signal of the tested steel wire rope;
step S30, comparing the preprocessed magnetic flux signal and magnetic leakage signal with a preset magnetic flux signal threshold and a preset magnetic leakage signal threshold respectively, and calculating the defect position;
step S40, extracting a defect magnetic flux signal and a defect magnetic flux leakage signal according to the defect position;
step S50, calculating the defect width flw of the detected steel wire rope according to the defect magnetic flux signal and the defect magnetic flux leakage signal;
step S60, calculating the defect section loss fs of the detected steel wire rope according to the defect width flw of the detected steel wire rope;
step S70, distinguishing internal and external defects:
calculating a waveform peak value FV of the defect magnetic leakage signal by using a formula FV-FL, wherein FH is the waveform peak value of the defect magnetic leakage signal, and FL is the waveform valley value of the defect magnetic leakage signal;
designing a relation function ffs as f2(FV, flw), wherein f2 is a training multiple equation group or a multilayer neural network, and ffs is a virtual section loss amount;
substituting the defect width flw obtained in the step S50 and the waveform peak value FV of the defect magnetic leakage signal into the design relation function to calculate a virtual section loss amount ffs;
comparing ffs and fs, if | fs-ffs | is more than mu, the defect is an internal defect, otherwise, the defect is an external defect;
where μ is a preset defect determination value.
2. The nondestructive testing method for detecting and distinguishing the internal and external defects of a steel wire rope according to claim 1, characterized in that: further comprising:
step S80, calculating defect burying depth:
according to the result of S70, if the defect is an external defect, the buried depth of the defect is 0, otherwise the buried depth of the defect is not 0, performing the following steps;
the buried depth fd of the defect is calculated according to the following formula,
fd＝f3(fs,ffs,flw)，
wherein f3 is a multiple equation group or a multilayer neural network of training, flw is the defect width of the steel wire rope to be detected, fs is the defect section loss of the steel wire rope to be detected, and ffs is the virtual section loss.
3. The nondestructive testing method for detecting and distinguishing the internal and external defects of a steel wire rope according to claim 2, characterized in that:
in step S70, the trained multiple equation set or the multi-layer neural network f2 is obtained by the following steps:
step 721: designing x defect widths, y defect section loss amounts and total x multiplied by y standard surface defects, wherein x and y are natural numbers;
step 722: calculating the x y standard surface defects through the steps S10 to S70 to obtain corresponding peak values of the leakage flux waveform of the defects;
step 723: training to obtain a multiple equation set or a multilayer neural network f2 by taking the obtained defect leakage magnetic waveform peak value and the defect width of the standard surface defect as input independent variables and ffs as output standard quantities;
in step S80, the trained multiple equation set or the multi-layer neural network f3 is obtained by the following steps:
step 821: designing x defect widths, y defect section loss amounts, z different buried depths, and total x multiplied by y multiplied by z standard defects, wherein x, y and z are natural numbers;
step 822: calculating the standard defects of x, y and z to obtain corresponding ffs through the steps S10 to S70;
step 823: and training to obtain a multiple equation set or a multilayer neural network f3 by taking the obtained ffs, the defect section loss amount and the defect width as input independent variables and the defect burial depth as an output standard quantity.
4. The nondestructive testing method for detecting and distinguishing the internal and external defects of a steel wire rope according to claim 2, characterized in that:
step S10 includes that a magnetic flux signal of a steel wire rope to be detected is obtained through a magnetic flux detection sensor, a magnetic leakage signal of the steel wire rope to be detected is obtained through a magnetic field intensity detection sensor, and the obtained magnetic flux signal of the steel wire rope to be detected is subjected to primary processing by adopting the following steps, so that the influence of the speed of the steel wire rope is eliminated;
the magnetic flux signal is processed by integrating the integrator with time according to the following formula, then the data is collected by sampling in equal space,
wherein Y isiThe magnetic flux signal of the detected steel wire rope after the initial processing is obtained, S is the collected magnetic flux signal of the detected steel wire rope, dt represents the differential of time, and N is the total sampling point number;
or the following formula is adopted to carry out equidistant integration processing and acquisition on the magnetic flux signals through the space of the integrator;
y ═ Sdl, where Y is the magnetic flux signal of the detected steel wire rope after the preliminary processing, dl represents the differential to the spatial distance, and S is the collected magnetic flux signal of the detected steel wire rope.
5. The nondestructive testing method for detecting and distinguishing the internal and external defects of a steel wire rope according to claim 4, characterized in that:
the step S20 of preprocessing the magnetic flux signal of the steel wire rope to be detected comprises the steps of carrying out outlier rejection, noise filtering and baseline elimination on the magnetic flux signal of the steel wire rope to be detected;
the step of removing the wild points of the magnetic flux signals of the detected steel wire rope comprises the following steps:
removing wild points of the magnetic flux signal Y of the steel wire rope to be detected, setting Y (i) as an ith magnetic flux acquisition signal, and when Y (i) is far greater than the values of the front and rear magnetic flux signals, Y (i-1) + Y (i +1)](i is 1,2, …, N), and obtaining a signal Y after the outlier rejection processing1(i) N is the total number of sampling points;
the step of carrying out noise filtering on the magnetic flux signal of the steel wire rope to be detected comprises the following steps:
and performing noise filtering on the magnetic flux signal of the steel wire rope to be detected by adopting self-adaptive filtering, wavelet transformation, smoothing filtering or empirical mode decomposition, wherein a calculation formula for performing noise filtering on the magnetic flux signal of the steel wire rope to be detected by adopting smoothing filtering is as follows:
wherein N is the number of data for averaging, and N is the total number of sampling points;
the step of eliminating the magnetic flux signal of the tested steel wire rope by the base line comprises the following steps:
performing baseline elimination on the magnetic flux signal of the steel wire rope to be detected by adopting envelope spectrum extraction, wavelet decomposition, window averaging or empirical mode decomposition, wherein the step of performing baseline elimination on the magnetic flux signal of the steel wire rope to be detected by adopting empirical mode decomposition comprises the following steps of:
finding the signal data sequence Y2(i) Fitting all the maximum value points and minimum value points to an upper envelope line and a lower envelope line of the original sequence by a cubic spline function respectively; the mean of the upper and lower envelopes is m 1; data sequence Y2(i) Subtracting m1 to obtain a new sequence Y with low frequency subtracted3(i) I.e. Y3(i)＝Y2(i)－m1；
In step S20, preprocessing the magnetic leakage signal of the steel wire rope to be tested includes performing outlier rejection, noise filtering, baseline elimination, and strand noise filtering on each magnetic leakage signal of the steel wire rope to be tested;
the step of eliminating the magnetic leakage signals of each path of the detected steel wire rope comprises the following steps:
eliminating the wild points of each path of magnetic leakage signal X, and setting Xi,jIs the jth sampling value of the ith Hall sensor when X isi,jWhen the magnetic flux leakage signal value is far larger than the front and back magnetic flux leakage signal values:
obtaining a signal X after the outlier rejectioni,j；
The step of carrying out noise filtering on each path of leakage magnetic signal of the tested steel wire rope comprises the following steps:
carrying out noise filtering on each path of magnetic flux leakage signal of the steel wire rope to be tested by adopting self-adaptive filtering, or wavelet transformation, or smooth filtering, or empirical mode decomposition; the calculation formula for carrying out noise filtering on each path of magnetic leakage signal of the detected steel wire rope by adopting smooth filtering is as follows:
wherein N is the number of data for averaging, N is the number of total sampling points, and k is the number of magnetic field intensity detection sensor paths for collecting the magnetic leakage signal of the steel wire rope to be detected;
the step of eliminating the baseline of each path of magnetic leakage signal of the tested steel wire rope comprises the following steps:
performing baseline elimination on each path of magnetic leakage signal of the tested steel wire rope by adopting envelope spectrum extraction, wavelet decomposition, window averaging or empirical mode decomposition; the step of eliminating the baseline of each path of magnetic leakage signal of the tested steel wire rope by adopting empirical mode decomposition comprises the following steps:
finding out all maximum value points and minimum value points of the magnetic leakage signal data sequence Xy after the outlier rejection processing, respectively fitting the maximum value points and the minimum value points to an upper envelope line and a lower envelope line of an original sequence by using a cubic spline function, wherein the mean value of the upper envelope line and the lower envelope line is n 1;
subtracting n1 from the original data sequence to obtain a new sequence X1 with a low frequency subtracted, namely X1-Xy-n 1;
the step of filtering the strand wave noise of each path of magnetic leakage signal of the tested steel wire rope comprises the following steps:
adopt wavelet decomposition, or empirical mode decomposition, or adaptive filtering, or the gradient method is right every way magnetic leakage signal of being surveyed wire rope carries out the spike noise filtering, wherein, adopt the gradient method right every way magnetic leakage signal of being surveyed wire rope carries out the step of spike noise filtering and includes:
using gradient method to realize first order differentiation of image, for image X1(x, y) whose gradient at coordinate (x, y) is a two-dimensional column vector representation:
the modulus of this vector is:
summing the multi-path magnetic leakage signals to obtain magnetic leakage sum signal X2。
6. The nondestructive testing method for detecting and distinguishing the internal and external defects of a steel wire rope according to claim 5, characterized in that:
the calculating of the defect position in step S30 includes the steps of:
step S31, setting a defect magnetic flux signal preset threshold value mp of the detected steel wire rope, wherein mp is the peak value of the magnetic flux signal with the minimum defect;
step S32, comparing the magnetic flux signal of the detected steel wire rope with a defect magnetic flux signal preset threshold value mp, and recording a plurality of groups of magnetic flux sampling points of which the continuous magnetic flux signals are greater than the defect magnetic flux signal preset threshold value mp, wherein the axial coordinates of the plurality of groups of magnetic flux sampling points are [ c11, c12 … … c1a ], [ c21, c22 … … c2b ] and … …;
step S33, calculating an average value of the axial coordinates of each set of magnetic flux collection points, where c1 is (c11+ c12+ … … + c1a)/a, c2 is (c21+ c22+ … … + c2b)/b, … …, to obtain a sequence (c1, c2, … …);
step S34, setting a defect magnetic leakage signal preset peak-to-peak value threshold vp of the detected steel wire rope, wherein vp is a magnetic leakage signal preset peak-to-peak value of the minimum defect;
comparing the magnetic leakage signal of the tested steel wire rope with a preset peak-to-peak threshold vp of the defect magnetic leakage signal, recording a plurality of groups of continuous magnetic leakage sampling points of which the magnetic leakage signals are greater than the preset peak-to-peak threshold vp of the defect magnetic leakage signal, wherein the axial coordinates of the plurality of groups of magnetic leakage sampling points are [ d11, d12 … … d1e ], [ d21, d22 … … d2f ] and … …, and obtaining sequences (d1, d2 and … …);
step S35, calculating an average value of the maximum and minimum values of the axial coordinates of each group of leakage flux collection points, namely d1 ═ d11+ d1e)/2, d2 ═ d21+ d2f)/2, … …;
and step S36, comparing the sequences (c1, c2, … …) with the sequences (d1, d2, … …), if the | ci-dj | is less than M, wherein M is the distance between steel wire strands, keeping ci and abandoning dj, otherwise, keeping both ci and dj, and obtaining the calculation result as the defect position.
7. The nondestructive testing method for detecting and distinguishing the internal and external defects of a steel wire rope according to claim 6, characterized in that: in step S40, the extracting the defect flux signal includes: according to the position information of each group of magnetic flux sampling points, FM points are extracted forwards and backwards from the magnetic flux signals of the steel wire rope to serve as defect magnetic flux signals, wherein FM is NO multiplied by SM, SM is the number of sampling points with 1 strand pitch, and NO is a natural number of 5-10;
extracting the defect leakage signal includes: according to the position information of each group of magnetic leakage acquisition points, LFM points are extracted forwards and backwards from the magnetic leakage signal of the steel wire rope to serve as a defect magnetic leakage signal, the LSM is the number of sampling points with 1 strand pitch, the LFM is LNO multiplied by LSM, and the LNO is a natural number of 5-10.
8. The nondestructive testing method for detecting and distinguishing the internal and external defects of a steel wire rope according to claim 7, characterized in that:
the step of calculating the defect width flw of the measured steel wire rope in the step S50 includes the steps of:
step S51, first, according to the formula h (S) ═ df (S)/ds (S) ═ 1,2, …, k), the differentiation result h (S) of the defect magnetic flux signal of the steel wire rope to be tested is solved, where k is the data number of the defect magnetic flux signal, and f (S) is the data of the defect magnetic flux signal; then, according to the position of the waveform peak point of the defect magnetic flux signal, the position of the maximum value of h(s) is taken forward, the position of the minimum value of h(s) is taken backward, and the distance between the maximum value and the minimum value is calculated as the waveform width value Ylw of the defect magnetic flux signal;
step S52, calculating the distance between the maximum value and the minimum value of the defect leakage magnetic signal according to the position of the waveform peak point of the defect leakage magnetic signal as the waveform width value Xlw of the defect magnetic flux signal;
s53, when the | Ylw-Xlw | is smaller than M, and M is the distance between steel wire strands, selecting the larger value of YLw and Xlw as the defect width flw; when | Ylw-Xlw | ≧ M, the defect width flw ═ (Ylw + Xlw)/2-LF, where LF is the distance of the sensor from the surface of the steel cord.
9. The nondestructive testing method for detecting and distinguishing the internal and external defects of a steel wire rope according to claim 8, characterized in that:
step S60, calculating the defect section loss fs of the detected steel wire rope comprises the following steps:
step S61, calculating a waveform peak value VPP of the obtained waveform peak value T and waveform baseline value L of the defect magnetic flux signal by a formula VPP ═ T-L |;
step S62, designing a relationship function fs ═ f1(VPP, flw), where f1 is a training multiple power equation group or a multilayer neural network;
step S63, substituting the defect width value flw obtained in the step S50 and the waveform peak value VPP of the defect magnetic flux signal obtained in the step S61 into the multiple power equation group or the multilayer neural network in the step S62, and calculating the defect section loss amount fs of the steel wire rope;
wherein the multi-degree program group or the multi-layer neural network f1 trained in the step S62 is obtained by the following steps:
step S621, designing x defect widths and y defect section loss amounts, wherein x is multiplied by y standard surface defects in total, and x and y are natural numbers;
step S622: calculating x y standard surface defects through steps S10 to S70 to obtain corresponding defect magnetic flux waveform peak-to-peak values;
step S623: and training to obtain a multiple equation set or a multilayer neural network f1 by taking the corresponding defect magnetic flux waveform peak value and the defect width of the standard surface defect as input independent variables and taking the defect section loss as output standard quantities.
10. The utility model provides a detect and distinguish nondestructive test device of inside and outside defect of wire rope which characterized in that, it includes:
the excitation structure is used for exciting the steel wire rope to a saturated or approximately saturated state;
the magnetic flux detection sensor is used for acquiring a magnetic flux signal of the steel wire rope to be detected;
the magnetic field intensity detection sensor is used for acquiring a magnetic leakage signal of the detected steel wire rope;
and a signal acquisition and processing system, wherein the signal acquisition and processing system is processed by the nondestructive detection method for detecting and distinguishing the internal and external defects of the steel wire rope according to any one of claims 2 to 9;
the signal acquisition and processing system comprises a signal acquisition unit, a signal preprocessing unit, a defect position calculating unit, a defect signal extracting unit, a defect width calculating unit, a defect section loss amount calculating unit, an internal and external defect distinguishing unit and a defect burying depth calculating unit, wherein,
the signal acquisition unit is used for acquiring a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be detected,
the signal preprocessing unit is used for preprocessing a magnetic flux signal and a magnetic leakage signal of the steel wire rope to be detected;
the defect position calculation unit is used for comparing the preprocessed magnetic flux signal and the preprocessed magnetic flux leakage signal with a preset magnetic flux signal threshold and a preset magnetic leakage signal threshold respectively to calculate the defect position;
the defect signal extraction unit is used for extracting a defect magnetic flux signal and a defect magnetic flux leakage signal according to the defect position;
the defect width calculation unit is used for calculating the defect width flw of the steel wire rope to be detected according to the defect magnetic flux signal and the defect magnetic leakage signal;
the defect section loss amount calculation unit is used for calculating the defect section loss amount fs of the detected steel wire rope according to the defect width flw of the detected steel wire rope;
the internal and external defect distinguishing unit is used for distinguishing whether the steel wire rope is an internal defect or an external defect according to the defect width flw of the steel wire rope to be detected and the waveform peak value FV of the defect magnetic flux signal;
the defect burying depth calculating unit is used for calculating the defect burying depth according to the defect width flw of the detected steel wire rope, the defect section loss fs and the virtual section loss ffs of the detected steel wire rope.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010529779.1A CN111579637B (en) | 2020-06-11 | 2020-06-11 | Nondestructive testing method and device for detecting and distinguishing internal and external defects of steel wire rope |
| EP21758301.2A EP3951381B1 (en) | 2020-06-11 | 2021-03-17 | Non-destructive testing method and device for testing and distinguishing internal and external defects of steel wire rope |
| US17/436,086 US11703477B2 (en) | 2020-06-11 | 2021-03-17 | Nondestructive testing method and device for detecting and distinguishing internal defect and external defect of wire rope |
| PCT/CN2021/081320 WO2021248962A1 (en) | 2020-06-11 | 2021-03-17 | Non-destructive testing method and device for testing and distinguishing internal and external defects of steel wire rope |
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| CN202010529779.1A CN111579637B (en) | 2020-06-11 | 2020-06-11 | Nondestructive testing method and device for detecting and distinguishing internal and external defects of steel wire rope |
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| EP (1) | EP3951381B1 (en) |
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| CN111579637B (en) | 2020-06-11 | 2022-04-29 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | Nondestructive testing method and device for detecting and distinguishing internal and external defects of steel wire rope |
| WO2022054315A1 (en) * | 2020-09-10 | 2022-03-17 | 株式会社島津製作所 | Wire rope inspection device and wire rope inspection system |
| CN112730602B (en) * | 2020-12-11 | 2024-04-23 | 浙江大学 | Device and method for detecting defects of suspended steel wires of front window sliding door of biosafety cabinet |
| CN114720552A (en) * | 2022-03-29 | 2022-07-08 | 哈尔滨理工大学 | Electromagnetic detection method for identifying defects of steel wire rope |
| KR102607277B1 (en) * | 2023-06-07 | 2023-11-29 | 한전케이피에스 주식회사 | System for detecting fault and method thereof |
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| US4659991A (en) * | 1983-03-31 | 1987-04-21 | Ndt Technologies, Inc. | Method and apparatus for magnetically inspecting elongated objects for structural defects |
| KR950013780B1 (en) * | 1990-05-22 | 1995-11-16 | 후루까와 뗀끼 고요교오 가부시끼가이샤 | Apparatus and method for measuring length of moving elongated object |
| US5565771A (en) * | 1995-01-18 | 1996-10-15 | Noranda, Inc. | Apparatus for increasing linear resolution of electromagnetic wire rope testing |
| US5804964A (en) * | 1996-11-29 | 1998-09-08 | Noranda Inc. | Wire rope damage index monitoring device |
| CA2616897C (en) | 2005-07-29 | 2015-06-16 | V & M Deutschland Gmbh | Method for error-free checking of tubes for surface faults |
| CN101216460B (en) | 2008-01-11 | 2011-01-26 | 清华大学 | AC and DC composite magnetisation based leakage detection recognition method for inside and outside wall defect |
| GB2492745A (en) * | 2011-06-06 | 2013-01-16 | Silverwing Uk Ltd | Magnetic flux leakage inspection |
| WO2013023818A1 (en) * | 2011-08-18 | 2013-02-21 | Nv Bekaert Sa | Magnetic flux leakage error detection in cords |
| US9103798B2 (en) * | 2011-12-07 | 2015-08-11 | Ndt Technologies, Inc. | Magnetic inspection device and method |
| CN102798667B (en) | 2012-08-16 | 2014-08-13 | 山东省科学院激光研究所 | Metal defect detection method of laser-electromagnetic ultrasonic nondestructive testing system |
| CN102798660B (en) | 2012-08-30 | 2015-11-11 | 东北大学 | Based on inner and outer walls of pipeline defect detecting device and the method for three axle leakage fields and current vortex |
| KR101482347B1 (en) * | 2012-12-27 | 2015-01-13 | 주식회사 포스코 | Apparatus and method of detecting inner defect of steel plate |
| DE102014212499A1 (en) * | 2014-06-27 | 2015-12-31 | Institut Dr. Foerster Gmbh & Co. Kg | Method and apparatus for leakage flux testing |
| CN104514987B (en) | 2014-12-19 | 2017-02-22 | 清华大学 | Three-dimensional pipeline flux leakage imaging defect quantizing method |
| RU2697061C1 (en) * | 2015-08-31 | 2019-08-09 | ДжейАрБи ИНДЖИНИРИНГ ПТИ ЛТД | Method and system for detecting discontinuity of material in magnetizable article |
| CN108828059B (en) * | 2018-06-29 | 2020-04-07 | 清华大学 | Electromagnetic multi-field coupling defect comprehensive detection and evaluation method and device |
| CN110006338B (en) * | 2019-04-28 | 2020-11-06 | 哈尔滨工业大学（深圳） | Method for detecting damage area of steel wire rope |
| CN110568059B (en) * | 2019-09-24 | 2022-07-19 | 哈尔滨工业大学（深圳） | Nondestructive testing method and device for steel wire rope |
| CN111024807B (en) * | 2019-12-18 | 2022-04-29 | 哈尔滨工业大学（深圳） | Steel wire rope magnetic flux damage detection and quantification method |
| CN111579637B (en) | 2020-06-11 | 2022-04-29 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | Nondestructive testing method and device for detecting and distinguishing internal and external defects of steel wire rope |
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| EP3951381C0 (en) | 2023-06-07 |
| US11703477B2 (en) | 2023-07-18 |
| EP3951381A1 (en) | 2022-02-09 |
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| US20220187246A1 (en) | 2022-06-16 |
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